Introduction

This project uses data from the Amherst College Emergency Medical Service (ACEMS) to explore trends in campus medical emergencies. We wanted to pick a topic which could convey some information about Amherst, hoping that a more personal topic would lead to interesting findings. The decision to use ACEMS data began with a conversation between the two of us. With one of us a senior member of ACEMS and the other an unaffiliated Amherst student, we noticed a strong disconnect in the amount of campus knowledge and awareness of ACEMS (Julius didn’t even know how many members there were!). And given Austin’s connection to the team, accessing data and creating informed statistics would be much easier. We hope that this topic will both increase knowledge of ACEMS’ activities and generally reflect some trends of Amherst’s campus life.

To tackle these goals, we address three central questions. First, where and when do campus emergencies typically occur? To answer, we summarize ACEMS responses, tracking the locations and types of calls which are most prevalent. Second, how can we use ACEMS data to measure COVID policy? We explore how effective restrictions were on minimizing social interaction — using the frequency of alcohol and narcotics calls as a proxy for campus socialization. Third, what is the purview of ACEMS? When should a responder expect to escalate a call to the Amherst Fire Department (AFD)? We analyze which types of calls are typically escalated.

Discussion of our data and findings will begin with explanation of data collection and wrangling. Then we will explore our three central goals. Lastly, we conclude and discuss implications for campus life.

Methodology

Content

You can include text, code, and output as usual. Remember to take full advantage of Markdown and follow our Style Guide.

Examples and additional guidance are provided below.

Take note of the the default code chunk options in the setup code chunk. For example, unlike the rest of the Rmd files we worked in this semester, the default code chunk option is echo = FALSE, so you will need to set echo = TRUE for any code chunks you would like to display in the blog. You should be thoughtful and intentional about the code you choose to display.

library(DT)
mtcars %>% 
  select(mpg, cyl, hp) %>% 
  datatable(colnames = c("MPG", "Number of cylinders", "Horsepower"),
            filter = 'top',
            options = list(pageLength = 10, autoWidth = TRUE))

library(kableExtra)
summary(cars) %>%
  kbl(col.names = c("Speed", "Distance"),
      row.names = FALSE) %>%
  kable_styling(bootstrap_options = "striped",
                full_width = FALSE) %>%
  row_spec(0, bold = TRUE) %>%
  column_spec(1:2, width = "1.5in") 

Images

Images and gifs can be displayed using code chunks:

This is a figure caption

You may also use Markdown syntax for displaying images as shown below, but code chunks offer easier customization of the image size and alignment.

This is another figure caption

Either way, the file path can be a local path within your project directory or a URL for an image hosted online. This syntax works for PNG, PDF, JPG, and even GIF formats.

Formatting

Customizing your blog design

As a final detail only if you have time, you can explore options for customizing the style of your blog. By default, we are using the readthedown theme from the rmdformats package (see Line 6 of this file if you want to switch out themes).

References

All data sources, any key R packages, and any other sources used in developing your blog should be cited in full in a list of references at the end of your blog. Your blog post should also link to these sources as they are discussed. You may choose any reference style as long as sources are fully cited (try to be consistent!).

Typically, references in R Markdown (and LaTeX) files are incorporated with a BibTeX database (a .bib file). You can try this approach or manually include either a numbered or alphabetized list.

Columbia University has compiled some guidance on how to cite data. Some data sources will give you the citation information to copy and paste. Use the provided citations or citation styles in those cases.

You can list R package citations with the code citation("packageName") in the console and then copy (and reformat as needed) the relevant text, e.g.,

## 
## To cite package 'DT' in publications use:
## 
##   Xie Y, Cheng J, Tan X (2023). _DT: A Wrapper of the JavaScript
##   Library 'DataTables'_. R package version 0.27,
##   <https://CRAN.R-project.org/package=DT>.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {DT: A Wrapper of the JavaScript Library 'DataTables'},
##     author = {Yihui Xie and Joe Cheng and Xianying Tan},
##     year = {2023},
##     note = {R package version 0.27},
##     url = {https://CRAN.R-project.org/package=DT},
##   }

The following citations are based on the American Statistical Association citation style (not all of these references are used in this document).:

Baumer, B. S., Kaplan, D. T., and Horton, N. J. (2021), Modern Data Science with R (2nd ed.), Boca Raton, FL: CRC Press.

Broman, K. W. and Woo, K. H. (2018), “Data Organization in Spreadsheets,” The American Statistician, 72:1, 2-10, doi: 10.1080/00031305.2017.1375989

Columbia University Libraries (n.d.), “Data Citation,” available at https://guides.library.columbia.edu/datacitation.

McNamara, A. and Horton N. J. (2018) “Wrangling Categorical Data in R,” The American Statistician, 72:1, 97-104, doi: 10.1080/00031305.2017.1356375.

Shah, Syed A. A. (October 2022), “Starbucks Drinks” (Version 1), Kaggle, available at https://www.kaggle.com/datasets/syedasimalishah/starbucks-drinks.

Xie Y, Cheng J, Tan X (2022). “DT: A Wrapper of the JavaScript Library ‘DataTables’,” R package version 0.24, available at https://CRAN.R-project.org/package=DT.